Robust multichannel predictive deconvolution based on the alternating split Bregman iteration algorithm
Li Zhongxiao1,2, Li Zhenchun1,2
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China;
2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266071, China
Abstract:This paper introduces the L1 norm minimization constraint of primaries into the multichannel predictive deconvolution and proposes a robust multichannel predictive deconvolution based on the alternating split Bregman iteration algorithm.The proposed method utilizes the proximity operator to solve the optimization problem of the L1 norm minimization.Moreover,since the proposed method only needs the matrix inversion once during the whole iteration procedure,it has low computational complexity.This paper introduces the mathematical model of the multichannel predictive deconvolution,then gives the optimization problem of the robust multichannel predictive deconvolution,and elaborates the iteration steps of the alternating split Bregman iteration algorithm for solving the optimization problem.Compared with the robust multichannel predictive deconvolution method based on the iterative reweighted least squares algorithm,the proposed method can improve the computation efficiency while achieving similar accuracy.Moreover,compared with the multichannel predictive deconvolution method based on the least squares and the robust single channel predictive deconvolution method based on the alternating split Bregman iteration algorithm,the proposed method can better balance primary preservation and multiple removal.In addition,the proposed method utilizes the advantage of the multichannel predictive deconvolution and can better accommodate the fluctuant change of the sea floor than the single channel predictive deconvolution.Tests on synthetic and real data demonstrate that the proposed method can effectively suppress water layer multiples while preserving primaries in the case that the water layer multiples exhibit periodicity.Additionally,the proposed method has high computational efficiency.However,it is hard to judge intuitively multiple removal effects if the periodicity of water layer multiples is not satisfied well.
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